Github Deep Research

GitHub Deep Research

Multi-round research combining GitHub API, web_search, and browse_page to produce comprehensive markdown reports on any GitHub repository.

Poirot note: The original deer-flow skill uses a bundled scripts/github_api.py helper. Poirot doesn't bundle that script, so this version uses bash with curl to the GitHub API directly + gh CLI when available.

When to Use

• User provides a GitHub repository URL
• User asks for comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of an open source project
• User wants to understand a project's architecture, history, or community

Research Workflow

• Round 1: GitHub API (repo metadata, README, file tree, contributors, commits)
• Round 2: Discovery (web search for overview, competitors)
• Round 3: Deep Investigation (architecture, timeline, community sentiment)
• Round 4: Deep Dive (commit history, issues/PRs for feature evolution)

Round 1 — GitHub API

Setup

# Resolve owner/repo from remote URL
REMOTE_URL=$(git remote get-url origin 2>/dev/null || echo "")
# Or user provides owner/repo directly
OWNER="owner"
REPO="repo"

Repo metadata via curl

# Repo summary
curl -s "https://api.github.com/repos/$OWNER/$REPO" | python3 -c "
import sys, json
r = json.load(sys.stdin)
print(f'Name: {r[\"full_name\"]}')
print(f'Description: {r[\"description\"]}')
print(f'Stars: {r[\"stargazers_count\"]}')
print(f'Forks: {r[\"forks_count\"]}')
print(f'Language: {r[\"language\"]}')
print(f'License: {r.get(\"license\",{}).get(\"spdx_id\",\"N/A\")}')
print(f'Created: {r[\"created_at\"][:10]}')
print(f'Updated: {r[\"updated_at\"][:10]}')
"

# README
curl -s "https://api.github.com/repos/$OWNER/$REPO/readme" | python3 -c "
import sys, json, base64
r = json.load(sys.stdin)
print(base64.b64decode(r['content']).decode('utf-8'))
"

# Recent commits
curl -s "https://api.github.com/repos/$OWNER/$REPO/commits?per_page=10" | python3 -c "
import sys, json
for c in json.load(sys.stdin):
    print(f'{c[\"sha\"][:7]} {c[\"commit\"][\"author\"][\"date\"][:10]} {c[\"commit\"][\"message\"].splitlines()[0][:80]}')
"

# Languages
curl -s "https://api.github.com/repos/$OWNER/$REPO/languages"

# Contributors
curl -s "https://api.github.com/repos/$OWNER/$REPO/contributors?per_page=10" | python3 -c "
import sys, json
for c in json.load(sys.stdin):
    print(f'{c[\"login\"]:20s} {c[\"contributions\"]} commits')
"

Via gh CLI (if available)

gh repo view $OWNER/$REPO
gh api repos/$OWNER/$REPO/commits --paginate | head -50

Round 2 — Discovery (3-5 web_search)

• Get overview and identify key terms
• Find official website/docs
• Identify main players/competitors

Round 3 — Deep Investigation (5-10 web_search + browse_page)

• Technical architecture details
• Timeline of key events
• Community sentiment
• Use browse_page on valuable URLs for full content

Round 4 — Deep Dive

• Analyze commit history for timeline
• Review issues/PRs for feature evolution
• Check contributor activity

Report Structure

1. Metadata Block — Date, confidence level, subject
2. Executive Summary — 2-3 sentence overview with key metrics
3. Chronological Timeline — Phased breakdown with dates
4. Key Analysis Sections — Topic-specific deep dives
5. Metrics & Comparisons — Tables, growth charts
6. Strengths & Weaknesses — Balanced assessment
7. Sources — Categorized references
8. Confidence Assessment — Claims by confidence level

Confidence Scoring

Citation Format

Always include inline citations: [citation:Title](URL) immediately after each claim from external sources.

Output

Save report as: .poirot/outputs/research_{topic}_{YYYYMMDD}.md

Best Practices

1. Start with official sources — Repo, docs, company blog
2. Verify dates from commits/PRs — More reliable than articles
3. Triangulate claims — 2+ independent sources
4. Note conflicting info — Don't hide contradictions
5. Distinguish fact vs opinion — Label speculation clearly
6. Always include inline citations